def plotData(self, filename="temp-plot"):
xLinspace = np.linspace(0, self.getDataLengthInSec(), self.data.size)
Plotter.scatter(
x=xLinspace,
y=self.data,
layout=go.Layout(
title=self.dataSource,
xaxis=dict(
title='Czas trwania [s]'
),
yaxis=dict(
title='Wartość próbki'
)
),
filename=filename
)
selection = 'ee'
#era = "2016"
era = "2017"
#era = "2018"
config, pathMVA, path, figpath, pathSelections = setConfiguration(
selection, era)
figpath = dirStructure(figpath)
Help = Helper(era)
Help.SetPath(path)
Help.figpath = figpath
Plotter = Plotter()
Plotter.SetFig(Help.figpath)
Cut = Cuts(path=Help.path)
# In[9]:
Read = Reader(config)
# In[ ]:
data = Read.read(reduce=False)
# In[ ]:
# # Analyser
#bool_index = Testing_data_ndarray['Brho'] > 1.1
#Testing_data_ndarray_cut = Testing_data_ndarray[bool_index] #selection over all branches
#ThetaLdeg = Testing_data_ndarray['ThetaLdeg']
#ThetaLdeg_index = np.where(ThetaLdeg > 0.0) #obtain the indices for this condition
#ThetaLdeg = ThetaLdeg[ThetaLdeg > 0.0] #cut only one variable
#Brho = Testing_data_ndarray['Brho']
#Brho = Brho[ThetaLdeg_index] #cut another variable
'''
----------------------------------------------------------------- Plot variables ------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------- Using "Plotter" Class ---------------------------------------------------------------------------------------------------------------
'''
################################################ Histo_1D() function ##########################################################################################
Histo1D_object = Plotter([
Testing_data_hdf5['Brho'][()]
]) #Create the base with the variables in a Histo1D_object)
######### Some options
Histo1D_object.SetFigSize(10, 7)
Histo1D_object.SetBinX(100)
Histo1D_object.SetFigTitle('Variable 1 histogram 1D', 20)
Histo1D_object.SetLimX((0.0, 2.0))
Histo1D_object.SetLabelX('Variable 1', 15)
######### Generate the histogram
Histo1D_object.Histo_1D()
######### Save and show the created figure
Histo1D_object.SetOutDir(basepath + 'Modules/' + MODULE_name +
'/Outputfiles/Figures/')
from Models.Models import *
from Models.Scheduler import Scheduler
# Definir se os dados
if __name__ == '__main__':
models = dict(
NaiveBayes=NaiveBayes(),
LightGbm=LightGbm(),
#AdaBoost=AdaBoost(),
#RandomForest=RandomForest(),
#RegLogistica=Logistic(),
)
handler = Handler()
plot = Plotter()
fe = FeatureEng()
# Le dados de resmple - Só pra não demorar muito
df_selected_features, df_target, X_test = handler.get_selected_features()
df_target = np.ravel(df_target)
# Feature Enge
df_selected_features = fe.generate_feature(df=df_selected_features)
X_test = fe.generate_feature(df=X_test)
# Divisão do datset para validacao do modelo
X_train, X_valid, y_train, y_valid = train_test_split(
df_selected_features,
df_target,
#componentList = bkgSamples + sigSamples
#componentList = bkgSamples
componentList = sigSamples
for dataset in componentList:
if dataset.isMC:
dataset.lumi = 77.30
for component in dataset.componentList:
component.maxEvents = nEvents
xsWeighter = XSWeighter("XSWeighter")
preskimmer = GENPreskimmer("Preskimmer")
preskimCounter = GenWeightCounter("GenWeightCounter", postfix="Preskim")
fidskimmer = FiducialSkimmer("FiducialSkimmer")
fidCounter = GenWeightCounter("GenWeightCounter", postfix="FidicialCut")
plotter = Plotter("Plotter", validation_plots)
sequence = Sequence()
sequence.add(xsWeighter)
sequence.add(preskimmer)
sequence.add(preskimCounter)
sequence.add(fidskimmer)
sequence.add(fidCounter)
#sequence.add(plotter)
cutflows = [
"Preskim",
"FidicialCut",
]
endModuleOutputDir = "/home/lucien/public_html/" + out_path
import numpy as np
import pandas as pd
from Handler.Handler import Handler
from FE.FE import FeatureEng
from Plotter.Plotter import Plotter
# Feature Selection
plot = Plotter()
handler = Handler()
fe = FeatureEng()
# Le dados de resmple - Só pra não demorar muito
df_train, df_target = handler.get_resample()
df_target = np.ravel(df_target)
# Usar metodos mais avançados
df_selected_features, df_importances = fe.check_feature_importance(X=df_train,
y=df_target)
# Ordena dados
df_importances.sort_values(by=['importance'], inplace=True)
df_importances['threshold'] = df_importances['importance'].mean()
plot.create_bar_plot_h(df=df_importances, x='variable', y='importance')
else:
print('No assumed')
# Generowanie wykresów
if input("Do you want to generate charts? [y/n]:") not in ['no', 'n', 'nah']:
generator.plotData(filename="plots/guitar.html")
conn = sql.connect('plots/db/results.db')
### PSK
result = conn.execute(
"""
SELECT * FROM results WHERE description=?
""", ("PSK - BER to (order, snr)", )).fetchall()
Plotter.OrderSnr(result, modType="PSK", filename="plots/PSK/OrderSnr.html")
Plotter.OrderSnr2D(result,
modType="PSK",
filename="plots/PSK/OrderSnr2D.html")
result = conn.execute(
"""
SELECT * FROM results WHERE description=?
""", ("PSK - BER to (order, sample frequency)", )).fetchall()
Plotter.OrderSampFreq(result,
modType="PSK",
filename="plots/PSK/OrderSampleFreq.html")
Plotter.OrderSampFreq2D(result,
modType="PSK",
filename="plots/PSK/OrderSampleFreq2D.html")
histo, bin_edges = np.histogram(data_14_degrees['M_Q'][condition_mass_14_q], bins=bins)
bin_edges = bin_edges[1:]
fig = plt.figure(figsize=(12,7))
plt.plot(bin_edges, histo)
peaks, _ = find_peaks(histo, distance = distance, height=height)
plt.plot(bin_edges[peaks], histo[peaks], "x")
texto = 'Selection:\n Z>0 \n Zi>0 \n 100<M<160 \n 2.6<M_Q<3.5 \n -100<Pf<100 \n -110<Yf<50 \n Xf>-1500 \n MW_Nr>=0 \n Qi==%i' %q
plt.text(0.9, 0.8, s=texto, fontsize=12, color='black', transform=plt.gcf().transFigure, bbox=dict(facecolor='white', edgecolor='blue', pad=8.0), ha='center', va='center')
plt.savefig(basepath + 'Modules/' + MODULE_name + '/Outputfiles/Figures/14_degrees_peaks/' + '14_degrees_peaks_q_%i.png' %q)
print(q)
plt.close('all')
plt.close(fig)
M_vs_M_Q_14_pattern_q = Plotter([data_14_degrees['M_Q'][condition_mass_14_q],data_14_degrees['M'][condition_mass_14_q]]) #Create the base with the variables in a object
M_vs_M_Q_14_pattern_q.SetFigSize(12,7)
M_vs_M_Q_14_pattern_q.SetBinX(500)
M_vs_M_Q_14_pattern_q.SetBinY(500)
M_vs_M_Q_14_pattern_q.SetFigTitle(r'M vs M_Q with pattern and q = %i 14$\degree$' %q, 20)
M_vs_M_Q_14_pattern_q.SetLabelX('M_Q', 20)
M_vs_M_Q_14_pattern_q.SetLabelY('M', 20)
M_vs_M_Q_14_pattern_q.SetSizeTicksX(10)
M_vs_M_Q_14_pattern_q.SetBoxText('Selection:\n Z>0 \n Zi>0 \n 100<M<160 \n 2.6<M_Q<3.5 \n -100<Pf<100 \n -110<Yf<50 \n Xf>-1500 \n MW_Nr>=0 \n Qi==%i' %q)
M_vs_M_Q_14_pattern_q.SetLimX((2.4,3.6))
M_vs_M_Q_14_pattern_q.SetLimY((q*2.5,q*4.0))
M_vs_M_Q_14_pattern_q.Histo_2D() #Draw it
#plot pattern charge
plt.plot(q_x, q_y, '.-k', label='q = %i' %q)
plt.legend()
LambdaFunc('x: x.m_asym_bl[0]')),
Plot("m_ct_prop", ["TH1D", "m_ct_prop", "", 16, 0., 800.],
LambdaFunc('x: x.m_ct[0]')),
]
xsWeighter = XSWeighter("XSWeighter")
mediumMuonProducer = PhysObjProducer("MediumMuonProducer", "Muon",
"MediumMuons", "Moriond17MediumMuon")
mediumElectronProducer = PhysObjProducer("MediumElectronProducer", "Electron",
"MediumElectrons",
"Moriond17MediumElectron")
jetProducer = JetProducer("JetProducer", "Jet",
["MediumMuons", "MediumElectrons"], "LooseJets",
"Moriond17LooseJet", 0.4)
if ratio_switch:
plotter = Plotter("Plotter", ratio_plots)
else:
plotter = Plotter("Plotter", mc_plots)
anaProducer = AnalysisProducer("AnaProducer")
sequence = Sequence()
sequence.add(mediumMuonProducer)
sequence.add(mediumElectronProducer)
sequence.add(jetProducer)
sequence.add(xsWeighter)
sequence.add(anaProducer)
sequence.add(plotter)
endSequence = EndSequence(skipHadd=False)
if not ratio_switch:
endModuleOutputDir = "/home/kshi/public_html/mcPlot/"
from Plotter.Plotter import Plotter
from Plotter.PlotEndModule import PlotEndModule
from RA5.Config.Plotter.PlotDefinition import allPlots
nCores = 8
outputDir = "/raid/raid7/lucien/SUSY/RA5/Log/MCDistributions/2018-05-15/AllMCSamples_v1/"
nEvents = -1
disableProgressBar = False
justEndSequence = False
componentList = allMCSamples
for dataset in componentList:
dataset.lumi = 35.9
sequence = Sequence()
xsWeighter = XSWeighter("XSWeighter")
sequence.add(xsWeighter)
sequence.add(mediumMuonProducer)
sequence.add(looseMuonProducer)
sequence.add(mediumElectronProducer)
sequence.add(looseElectronProducer)
sequence.add(jetProducer)
sequence.add(Plotter("Plotter", allPlots))
endSequence = EndSequence()
endModuleOutputDir = "/home/lucien/public_html/SUSY/RA5/Log/MCDistributions/2018-05-15/AllMCSamples_v1/"
endSequence.add(PlotEndModule(endModuleOutputDir, allPlots))
outputInfo = OutputInfo("OutputInfo")
outputInfo.outputDir = outputDir
outputInfo.TFileName = "MCDistributions.root"
Pf_21 = data_21_degrees['Pf']; Tf_21 = data_21_degrees['Tf'] #mrad
Pf_14_21 = data_14_21_degrees['Pf']; Tf_14_21 = data_14_21_degrees['Tf'] #mrad
###################################### conditions #########################################
############################# original conditions: Z>0, Zi>0, M>0 #########################
condition_14 = (data_14_degrees['Xf'][:] > -1500) & (data_14_degrees['Yf'][:] > -1500) #it is a boolean index to apply in a dataset
condition_21 = (data_21_degrees['Xf'][:] > -1500) & (data_21_degrees['Yf'][:] > -1500) #it is a boolean index to apply in a dataset
condition_14_21 = (data_14_21_degrees['Xf'][:] > -1500) & (data_14_21_degrees['Yf'][:] > -1500) #it is a boolean index to apply in a dataset
'''
----------------------------------------------------------------- Plot variables ------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------- Using "Plotter" Class ---------------------------------------------------------------------------------------------------------------
'''
################################################################## 14 degrees
Yf_vs_Xf_14 = Plotter([data_14_degrees['Xf'][condition_14], data_14_degrees['Yf'][condition_14]]) #Create the base with the variables in a object
Yf_vs_Xf_14.SetFigSize(12,7)
Yf_vs_Xf_14.SetBinX(500)
Yf_vs_Xf_14.SetBinY(500)
Yf_vs_Xf_14.SetFigTitle(r'Yf:Xf 14$\degree$', 20)
Yf_vs_Xf_14.SetLabelX('Xf [mm]', 20)
Yf_vs_Xf_14.SetLabelY('Yf [mm]', 20)
Yf_vs_Xf_14.SetSizeTicksX(10)
Yf_vs_Xf_14.SetBoxText('Selection:\n Z>0 \n Zi>0 \n M>0 \n Xf>-1500 \n Yf>-1500')
Yf_vs_Xf_14.Histo_2D() #Draw it
######### Save and show the created figure
Yf_vs_Xf_14.SetOutDir(basepath + 'Modules/' + MODULE_name + '/Outputfiles/Figures/')
Yf_vs_Xf_14.SaveFig('Yf_vs_Xf_14')
Yf_vs_Xf_14.Show(1) #show during 1 seconds, the close authomatically
Yf_vs_Xf_14.Close() #close all windows, axes and figures running backend
del Yf_vs_Xf_14 #erase Yf_vs_Xf_14 (is an object)
#'photonOneScRawE','photonOnePreShowerE','photonOneScBrem',
#'genPhotonPt',
#'vetoDY','genIsoPass',
#'dileptonPt','dileptonEta','dileptonPhi','dileptonM',
#'nMuons','nElectrons','nTaus','nPhotons','nJets','nBJets',
#'ProbeIDPass','ProbeISOPass','ProbeWorstPass','ProbeSigPass','ProbeIsConv',
]
path = "/home/jcordero/CMS/data_"+era+"/"+DataGen+"/SMP_ZG/Files/"+selection+"/"+SampleSet+"/"
figpath = "/home/jcordero/CMS/JYCMCMS/SMP_ZG/figs/"+era+"/"+DataGen+"/"+selection+"/"
pathSelections = path+"Reduced"
Help = Helper(era)
Help.figpath = figpath
Plotter = Plotter(Help=Help)
Plotter.SetFig(Help.figpath)
# # Save the Ranges and Bins from Class
# In[8]:
var = 'ranges'
df = pd.DataFrame(getattr(Help,var))
df['part'] = df.index
path = "/home/jcordero/CMS/JYCMCMS/SMP_ZG/python/Plotter/"
df.to_csv(path+var+'.csv')
############################################# Save ALL variables once cut it
Group_name = 'All' #write "All" for all the variables.
RAS.Save_hdf5_or_ndarray_object_as_hdf5(Path_to_tree, ['data_runs_14'],
Group_name,
data_14) #put directly the data array
RAS.Save_hdf5_or_ndarray_object_as_hdf5(Path_to_tree, ['data_runs_21'],
Group_name,
data_21) #put directly the data array
'''
------------------------------------------------------------------------- Analysis ---------------------------------------------------------------------------------------------------------------
'''
################################################ 14 degrees ################################################################
data_hdf5_14 = RAS.Read_hdf5_file(Path_to_tree, 'data_runs_14')
M_vs_M_Q_14 = Plotter([data_hdf5_14['M_Q'], data_hdf5_14['M']
]) #Create the base with the variables in a object
M_vs_M_Q_14.SetFigSize(12, 7)
M_vs_M_Q_14.SetBinX(50)
M_vs_M_Q_14.SetBinY(50)
M_vs_M_Q_14.SetFigTitle(r'run97 M vs M_Q 14$\degree$', 20)
M_vs_M_Q_14.SetLabelX('M_Q', 20)
M_vs_M_Q_14.SetLabelY('M', 20)
M_vs_M_Q_14.SetSizeTicksX(10)
M_vs_M_Q_14.SetBoxText(
'Selection:\n Z>0 \n Zi>0 \n 70<M<180 \n 2.25<M_Q<4.0 \n -100<Pf<100 \n -110<Yf<50 \n Xf>-1500 \n MW_Nr>=0'
)
M_vs_M_Q_14.Histo_2D() #Draw it
######### Save and show the created figure
M_vs_M_Q_14.SetOutDir(basepath + 'Modules/' + MODULE_name +
'/Outputfiles/Figures/')
M_vs_M_Q_14.SaveFig('M_vs_M_Q_14_run97')
(data_21_degrees['Pf'][:] < 100))
condition_14_21 = ((data_14_21_degrees['Xf'][:] > -1500) &
(data_14_21_degrees['Yf'][:] > -1500)
& (data_14_21_degrees['Yf'][:] > -110) &
(data_14_21_degrees['Yf'][:] < 50)
& (data_14_21_degrees['Pf'][:] > -100) &
(data_14_21_degrees['Pf'][:] < 100))
'''
----------------------------------------------------------------- Plot variables ------------------------------------------------------------------------------------------------------------------
------------------------------------------------------------- Using "Plotter" Class ---------------------------------------------------------------------------------------------------------------
'''
################################################################## 14 degrees
M_vs_M_Q_14 = Plotter([
data_14_degrees['M_Q'][condition_14], data_14_degrees['M'][condition_14]
]) #Create the base with the variables in a object
M_vs_M_Q_14.SetFigSize(12, 7)
M_vs_M_Q_14.SetBinX(500)
M_vs_M_Q_14.SetBinY(500)
M_vs_M_Q_14.SetFigTitle(r'M:M_Q 14$\degree$', 20)
M_vs_M_Q_14.SetLabelX('M_Q', 20)
M_vs_M_Q_14.SetLabelY('M', 20)
M_vs_M_Q_14.SetSizeTicksX(10)
M_vs_M_Q_14.SetBoxText(
'Selection:\n Z>0 \n Zi>0 \n M>0 \n -100<Pf<100 \n -110<Yf<50 \n Xf>-1500'
)
M_vs_M_Q_14.Histo_2D() #Draw it
######### Save and show the created figure
M_vs_M_Q_14.SetOutDir(basepath + 'Modules/' + MODULE_name +
'/Outputfiles/Figures/Raw_figures/')
"PUWeighter",
os.environ["BASE_PATH"] +
"/DataMC/Pileup/puWeightsRun2016_NTrueInt_FullDataset--Cert_271036-284044_13TeV_23Sep2016ReReco_Collisions16_JSON.txt",
doNTrueInt=True,
applySystVariation=True)
xsWeighter = XSWeighter("XSWeighter")
mediumMuonProducer = PhysObjProducer("MediumMuonProducer", "Muon",
"MediumMuons", "Moriond17MediumMuon")
mediumElectronProducer = PhysObjProducer("MediumElectronProducer", "Electron",
"MediumElectrons",
"Moriond17MediumElectron")
jetProducer = JetProducer("JetProducer", "Jet",
["MediumMuons", "MediumElectrons"], "LooseJets",
"Moriond17LooseJet", 0.4)
if ratio_switch:
plotter = Plotter("Plotter", ratio_plots)
else:
plotter = Plotter("Plotter", data_plots)
anaProducer = AnalysisProducer("AnaProducer")
eventSkimmer = EventSkimmer("StopToBLepSkim", cutflow="stCR")
hltSkimmer = HLTSkimmer("HLTSkim", cutflow="stCR")
jsonSkimmer = JSONSkimmer("JSONSkim")
sequence = Sequence()
sequence.add(jsonSkimmer)
sequence.add(hltSkimmer)
sequence.add(mediumMuonProducer)
sequence.add(mediumElectronProducer)
sequence.add(jetProducer)
sequence.add(xsWeighter)
sequence.add(puWeighter)
#Plot("nJet", ["TH1D","nJet","",5,-0.5,4.5], LambdaFunc('x: x.njets_pt30_eta2p5[0]'), ),
]
#____________________________________________________________________________________________________
#
plots = muon_plots + general_plots + jet_plots + signif_plots# big list of Plot objects
#for plot in plots:
#plot.plotSetting.divideByBinWidth = True
for dataset in componentList:
if dataset.isMC:
dataset.lumi = lumi
for component in dataset.componentList:
component.maxEvents = nEvents
plotter = Plotter("Plotter",plots)
sequence = darkphoton_signal_unblind_sequence
sequence.add(plotter)
outputInfo = OutputInfo("OutputInfo")
outputInfo.outputDir = outputDir
outputInfo.TFileName = "DataMCDistribution_radiuscut"+radcutstr+"GeV.root"
endSequence = EndSequence(skipHadd=justEndSequence)
endModuleOutputDir = "/home/"+User+"/public_html/Higgs/HToZdZd/"+out_path
endSequence.add(PlotEndModule(endModuleOutputDir,plots,skipSF=True))
## Put an index.php file into Plots dir for easy visualization
copyFile('/home/'+User+'/',phpFile,endModuleOutputDir)
import pandas as pd import numpy as np from Handler.Handler import Handler from EDA.EDA import Eda from Plotter.Plotter import Plotter plot = Plotter() handler = Handler() df_train, _, df_target = handler.get_data(frac=1.0) df_target = pd.DataFrame(df_target) df_train = pd.DataFrame(df_train) # EDA eda = Eda() # Análise básica d = eda.get_describe(df=df_train) eda.check_imbalance(var_name='target', df=df_target)
